DSDE: Features

This case study demonstrates an estimated cost savings of AICD completions in six wells of more than $20 million in capital and operating expenses compared with a more conventional sliding side door completion to manage gas breakthrough.

Glynn Williams, CEO of Silixa, offers his take on the role fiber-optic technology will play in the rise of CO2 storage and on the firm’s progress in the tight-rock sector.

Data mining and modern data analysis techniques allow us to do a much better job in finding good field analogies where new approaches to well remediation and restimulation have proven to have a high probability of commercial success.

The SPE Annual Technical Conference and Exhibition (ATCE) is right around the corner. Check ahead of the conference to make sure you don't miss any of these highlights.

These SPE members will be recognized during the 2022 Annual Technical Conference and Exhibition in Houston in October.

Devon, Shell, and SM Energy offer some of their latest learnings from recent independent subsurface diagnostics projects. Their work underscores why this arena of technology has become a cornerstone for hydraulic-fracture design in tight-rock reservoirs.

The key element of hydraulic-fracture modeling is the prediction of the generated fracture geometries. Research conducted over the years has trickled down predictive software. Nevertheless, the ability to design optimal fracture treatments is hampered, as we cannot “see” the subsurface.

Drillers are working to find ways to break some bad data habits. Those problems can range from the use of multiple formulas to calculate mechanical specific energy to timekeeping systems where the clocks and the time records are often off.

Drillbotics announced the winners for the 2021–2022 competition. Students from the University of Stavanger (UiS) won the Group A competition and the Norwegian University of Science and Technology (NTNU) won the Group B competition.

Supervised learning has many commercial applications; however, such learning lacks the capability to generate new insights and knowledge. In contrast, unsupervised learning discovers the inherent structures in unlabeled data, thereby helping generate new insights and actionable knowledge from large volumes of data.